To learn the methodologies of data simulation, bootstrapping and permutation testing, that allow a fast solution to complex statistical models without deep knowledge of classical statistical topics. Introduce Bayesian networks as graphical structures for representing probabilistic relationships among many variables and for doing inference.
Introduction to the R language. Permutation tests. Jackknife. Parametric and non-parametric bootstrap. Causal networks and inference in Bayesian networks. Learning Bayesian network parameters.